Testing using real human sketches.  
To make GAN customization available to everyday users, we then test the effectiveness of our method on hand-drawn sketches from novice users. We collect cat and horse sketches from the Quickdraw [1]} dataset as training images. We first train models on a single sketch, and show success and failure cases in Figure REF . We note that the image to sketch translation network Photosketch [2]} used in our method is trained on a sketch style that is different from the Quickdraw dataset. Despite the difference in the styles, our method succeeds on sketches that are more contour-like and depict simple poses (e.g., cat head). However, there is headroom for improvement, to support a wider range of sketching styles and poses. In particular, the results are worse with sketches that have more abstract styles or complex poses. We also observe that performance on difficult cases can be improved by increasing the number of input user sketches, as shown in Figure REF . Also, we find that augmentation is essential for our method to be successful for user sketches. As shown in Figure REF , given the same input sketches, only the model trained with augmentation will faithfully generate images that match the sketches.
